Chapter 11 Technology to Communicate and Act Upon Analytics
ARTHUR: Camelot!
SIR GALAHAD: Camelot!
LANCELOT: Camelot!
PATSY: It’s only a model.
Monty Python and the Holy Grail
Introduction
In the previous chapter, we discussed the architectural components required for data and analytics. We discussed considerations that the data scientist needs to think about when creating a workflow, capabilities required for data science and machine learning (ML) architectures, Auto ML, the increasing relevance of features stores, technical debt, and cost considerations when evaluating platforms.
Now that we understand the architectural components required to build data science and ML workflows, we now need to turn our attention ...
Get It's All Analytics - Part II now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
